Structure-Property Relationships in Benzofurazan Derivatives: A Combined Experimental and DFT/TD-DFT Investigation

In this work we seek to understand and to quantify the reactivity of benzofurazan derivatives toward secondary cyclic amines, like pyrrolidine, piperidine and morpholine, acting as nucleophile groups in SNAr reactions. For this aim, physico-chemical and structural descriptors were determined experimentally and theoretically using the DFT/B3LYP/6-31+ g (d,p) methodology. Thus, different 4-X-7-nitrobenzofurazans (X = OCH3, OC6H5 and Cl) and products corresponding to the electrophilic aromatic substitution by pyrrolidine, piperidine and morpholine, were investigated. Particularly, the HOMO and LUMO energy levels of the studied compounds, determined by Cyclic Voltammetry (CV) and DFT calculations, were used to evaluate the electrophilicity index (ω). The latter was exploited, according to Parr’s approach, to develop a relationship which rationalizes the kinetic data previously reported for the reactions of the 4-X-7-nitrobenzofurazans with nucleophiles cited above. Moreover, the Parr’s electrophilicity index (ω) of these benzofurazans determined in this work were combined with their electrophilicity parameters (E), reported in preceding papers, was found to predict the unknown electrophilicity parameters E of 4-piperidino, 4-morpholino and 4-pyrrolidino-7-nitrobenzofurazan. In addition, the relationship between the Parr’s electrophilicity index (ω) and Hammett constants σ, has been used as a good model to predict the electronic effect of the nucleophile groups. Finally, we will subsequently compare the electrophilicity index (ω) and the electrophilicity parameters (E) of these series of 7-X-4-nitrobenzofurazans with the calculated dipole moment (μ) in order to elucidate general relationships between E, ω and μ

Predictive Modeling of Groundwater Recharge under Climate Change Scenarios in the Northern Area of Saudi Arabia

Water scarcity is considered a major problem in dry regions, such as the northern areas of Saudi Arabia and especially the city of Hail. Water resources in this region come mainly from groundwater aquifers, which are currently suffering from high demand and severe climatic conditions. Forecasting water consumption as accurately as possible may contribute to a high level of sustainability of water resources. This study investigated different Machine Learning (ML) algorithms, namely Support Vector Machine (SVM), Random Forest (RF), Linear Regression (LR), and Gradient Boosting (GB), to efficiently predict water consumption in such areas. These models were evaluated using a set of performance measures, including Mean Squared Error (MSE), R-squared (R2), Mean Absolute Error (MAE), Explained Variance Score (EVS), Mean Absolute Percentage Error (MAPE), and Median Absolute Error (MedAE). Two datasets, water consumption and weather data, were collected from different sources to examine the performance of the ML algorithms. The novelty of this study lies in the integration of both weather and water consumption data. After examining the most effective features, the two datasets were merged and the proposed algorithms were applied. The RF algorithm outperformed the other models, indicating its robustness in capturing water usage behavior in dry areas such as Hail City. The results of this study can be used by local authorities in decision-making, water consumption analysis, new project construction, and consumer behavior regarding water usage habits in the region

A Generalized <italic>Supertwisting</italic> Algorithm Based on Terminal Sliding Manifold Control of Perturbed Unicycle Mobile Robots

This paper addresses the topic of stabilizing a class of nonholonomic systems in chained form impacted by matched uncertainties and time-varying perturbations. To design efficient stabilizing control law, the whole problem is divided into two sub-problems. The first sub-problem considers a system with perturbation stabilized using a fixed-time controller. The second sub-problem consists of the design of a controller for a second system operating under perturbations and uncertainties. To control this second system, firstly a terminal sliding mode manifold is proposed to achieve a fixed-time stability. As opposed to the traditional supertwisting algorithm (STA), the present work proposes a generalized STA (GSTA). The proposed method&#x2019;s most striking feature is the substitution of a fractional power term for the standard STA&#x2019;s discontinuous term, which can significantly boost the former&#x2019;s performance. It is demonstrated that the GSTA will reduce to the traditional STA if the fractional power in the nonsmooth term equals &#x2212;1/2. The ability of the sliding variables to finite-time converge to an arbitrary tiny region in the vicinity of the origin by adjusting the gains and the fractional power will be thoroughly proven under the GSTA by utilizing strict Lyapunov analysis. The effectiveness of the proposed control method is shown through extensive simulations

A Sustainable Fault Diagnosis Approach for Photovoltaic Systems Based on Stacking-Based Ensemble Learning Methods

In this study, a novel technique for identifying and categorizing flaws in small-scale photovoltaic systems is presented. First, a supervised machine learning (neural network) was developed for the fault detection process based on the estimated output power. Second, an extra tree supervised algorithm was used for extracting important features from a current-voltage (I–V) curve. Third, a multi-stacking-based ensemble learning algorithm was developed to effectively classify faults in solar panels. In this work, single faults and multiple faults are investigated. The benefit of the stacking strategy is that it can combine the strengths of several machine learning-based algorithms that are known to deliver good results on classification tasks, producing results that are more precise and efficient than those produced by a single algorithm. The approach was tested using an experimental dataset and the findings show that it could accurately diagnose faults (a detection rate of around 98.56% and a classification rate of around 96.21%). A comparison study with different ensemble learning algorithms (AdaBoost, CatBoost, and XGBoost) was conducted to evaluate the effectiveness of the suggested method

Fixed-Time Controller for Altitude/Yaw Control of Mini-Drones: Real-Time Implementation with Uncertainties

Gradually, it has become easier to use aerial transportation systems in practical applications. However, due to the fixed-length wire, recent studies on load-suspended transportation systems have revealed some practical constraints, especially when using quadrotor unmanned aerial vehicles (UAVs). By actively adjusting the distance between the quadrotor and the payload, it becomes possible to carry out a variety of challenging tasks, including traversing confined spaces, collecting samples from offshore locations, and even landing a payload on a movable platform. Thus, mass variable aerial transportation systems should be equipped with trajectory tracking control mechanisms to accomplish these tasks. Due to the above-mentioned reasons, the present paper addresses the problem of the altitude/yaw tracking control of a mini-quadrotor subject to mass uncertainties. The main objective of this paper is to design a fixed-time stable controller for the perturbed altitude/yaw motions, based on recent results using the fixed-time stability approach. For comparison reasons, other quadrotor motion controllers such as dual proportional integral derivative (PID) loops were considered. To show its effectiveness, the proposed fixed-time controller was validated on a real mini-quadrotor under different scenarios and has shown good performance in terms of stability and trajectory tracking

IoT-Based Low-Cost Photovoltaic Monitoring for a Greenhouse Farm in an Arid Region

In this paper, a low-cost monitoring system for an off-grid photovoltaic (PV) system, installed at an isolated location (Sahara region, south of Algeria), is designed. The PV system is used to supply a small-scale greenhouse farm. A simple and accurate fault diagnosis algorithm was developed and integrated into a low-cost microcontroller for real time validation. The monitoring system, including the fault diagnosis procedure, was evaluated under specific climate conditions. The Internet of Things (IoT) technique is used to remotely monitor the data, such as PV currents, PV voltages, solar irradiance, and cell temperature. A friendly web page was also developed to visualize the data and check the state of the PV system remotely. The users could be notified about the state of the PV system via phone SMS. Results showed that the system performs better under this climate conditions and that it can supply the considered greenhouse farm. It was also shown that the integrated algorithm is able to detect and identify some examined defects with a good accuracy. The total cost of the designed IoT-based monitoring system is around 73 euros and its average energy consumed per day is around 13.5 Wh

The N-alkylation of 4-nitrobenzochalcogenadiazoles: Synthesis and theoretical approach

International audienc

On Novel Fractional-Order Trajectory Tracking Control of Quadrotors: A Predefined-Time Guarantee Performance Approach

This paper presents a predefined-time fractional-order control for a quadrotor system subjected to perturbations. First, a fractional-order sliding manifold is proposed to ensure a predefined-time convergence of the tracking error. Second, a fractional-order switching-type predefined-time controller is proposed to achieve robustness against external disturbances. The predefined stability/convergence are proved using Lyapunov functions. The proposed method is validated using an adequate scenario and compared to other controllers to show the feasibility and superiority of the proposed one

An IoT Smart System for Cold Supply Chain Storage and Transportation Μanagement

Cold supply chains are becoming more and more attractive due to the high demand induced by increased consumption. To fulfill standards and customers’ requirements regarding the conditions under which cold supply chain products (mainly foods and pharmaceuticals) are stored (in warehouses) and transported to the end-users, tracking those conditions is a necessity. To ensure a high level of visibility, fostering emerging technologies can improve the quality of service in supply chains in terms of delivery time, cost, and quality. In this paper, a global framework for monitoring the conditions of storage and transportation of cold products across the whole supply chain is designed and implemented practically. The proposed solution is built around low-cost and low-energy consumption devices such as sensors and microcontrollers which are connected to cloud storage to allow a high level of visibility in the supply chain allowing all parties, including the end-consumers, to follow the products during their transfer, providing a conceptual framework that monitors the performance on a real-time basis and enhances decision making. A prototype using an embedded temperature/humidity sensor, a tiny microcontroller equipped with a Wi-Fi connectivity device, and a mobile 4G/5G network is designed and implemented. The proposed system is connected to a cloud-storage platform continuously accessible by the main parties of the cold supply chain including the provider, the transporter, and the end-consumer. The proposed framework may be handled as a smart contract during which any party can assume its responsibility for the assurance of the best conditions of the supply chain operation. A small-scale real-life scenario conducted in Jeddah City, Saudi Arabia is introduced to show the feasibility of the proposed framework

Electrophilicity, Mechanism and Structure-Reactivity Relationships of Cyclic Secondary Amines Addition to 2-Methoxy-3,5-dinitropyridine

International audienceThe second-order rate constants (k) for the nucleophilic aromatic substitution reactions of 2-methoxy-3,5-dinitropyridine 1 with secondary cyclic amines 2a-c in acetonitrile solution at 20°C are reported. The logarithms of these rate constants are particularly significant as possible measures of the electrophilicity parameters E for 2-methoxy-3,5-dinitropyridine 1 according to the linear free energy relationship log k (20°C) = sN(N + E). Additional kinetic data for reactions of 2-methoxy-3,5-dinitropyridine 1 with a series of nitroalkyl anions in DMSO solution are also measured and found to agree with those calculated by Mayr&#039;s approach from the known two solvent-dependent parameters N and sN of nitroalkyl anions and the electrophilicity parameter E measured in this work. A Single Electron Transfer (SET) mechanism is proposed for the SNAr reactions, on the basis of the linear Brönsted plot with the βnuc value of 0.94. The validity of this mechanism is confirmed by the agreement between the rate constants, k, and the oxidation potentials E° of these series of secondary cyclic amines. Also, reactivity descriptors such as the electronic chemical potential (μ), and the chemical hardness (η) for a series of pyridine derivatives were calculated by Density Functional Theory (DFT), and it is shown that the global electrophilicity index (ω = μ2/η) correlate significantly with the electrophilicity parameters E